Managing mechanical stress in sports participants

ABSTRACT

A method of predicting the probability of injuries in ice hockey which consists of analysing data from accelerometers and gyroscopes on the torso of a player and assessing the work load of the muscles of the player as an indicator of the probability of injury. One aspect provides a method of predicting the probability of groin injuries in ice hockey which consists of analysing data from accelerometers and gyroscopes on the torso of a player and assessing groin load as an indicator of the probability of groin injury. A second aspect provides method of managing player availability by limiting injuries which consists of analysing data from accelerometers and gyroscopes on the torso of a player and identifying and counting the number of slap shots executed as a means of assessing the player load as an indicator of probability of injury.

Priority is claimed under 35 U.S.C. § 119 to Australian PatentApplication No. 2015903183, filed Aug. 10, 2015, the disclosure of whichis incorporated herein by reference in its entirety.

This invention relates to methods of assessing workloads on sportsparticipants and in particular on ice hockey players and managing theirworkload to reduce the occurrence of injury.

BACKGROUND TO THE INVENTION

There is much information about the causes sports injuries and alsomethods of treating sports injuries including ice hockey injuries.

There are not many methods of diagnosing sports injuries.

U.S. Pat. No. 7,555,153 describes a diagnosis system in which imagingdata of musculoskeletal images are compared with categorised diseaseimages.

U.S. Pat. No. 8,636,627 discloses a mechanism for diagnosing ACLinjuries.

U.S. Patent Publication No. 20120276999 describes a foot pressuresensing system for use in sports training.

U.S. Patent Publication No. 20150040685 discloses a helmet sensor systemfor diagnosis of concussion injuries.

The number one soft tissue injury in ice hockey is groin pulls. Thistype of injury is also very hard to fully recover from, and thus limitsthe length of many players' careers.

Defensemen have a much higher rate of hip displacement injuries. It isan object of this invention to ameliorate these problems.

BRIEF DESCRIPTION OF THE INVENTION

To this end the present invention provides a method of predicting theprobability of injuries in ice hockey which consists of analysing datafrom accelerometers and gyroscopes on the torso of a player andassessing the work load of the muscles of the player as an indicator ofthe probability of injury.

The invention provides a system for managing ice hockey playeravailability which includes

player worn data loggers including accelerometers and gyroscopes, acomputer device adapted to receive data from said data loggers saidcomputer being programmed to analyse data from said accelerometers andgyroscopes and identifying and counting ice hockey player movements,measuring the work load associated with such movements, said computerdevice also being programmed to display the data.

The ice hockey player movement metrics include skating load, a count ofleft and right leg strides, groin load, high intensity skating strides,and slap shots, as well as other measures of the work load associatedwith such movements.

In one aspect the present invention provides a method of predicting theprobability of groin injuries in ice hockey which consists of analysingdata from accelerometers and gyroscopes on the torso of a player andassessing groin load as an indicator of the probability of groin injury.This enables player availability to be managed so that the probabilityof injuries can be reduced by using the groin load data to assess theprobability of injury. This information can be used to assist coachesand players to reduce the likelihood of groin injuries by providingmeans to identify and analyse the signals associated with the ice hockeystride.

This invention is predicated on the insight that there is a verydistinguishable pattern in a hockey stride when the side accelerometerdata is combined with the Gyroscope data. By identifying and trackingthis pattern, teams may be provided with insight into:

-   -   The total load being placed on the athletes groins from skating    -   Identify if there is a unilateral discrepancy between their        limbs—one leg produces more force than the other    -   Evaluate skating efficiency by counting the number of strides        needed to complete a specific course—important for player        evaluation and coaching.

In another aspect this invention provides a method of managing playeravailability by limiting injuries which consists of analysing data fromaccelerometers and gyroscopes on the torso of a player and identifyingand counting the number of slap shots executed as a means of assessingthe player load as an indicator of probability of injury.

Thus this invention may also be used to predict the probability ofinjuries by using the slap shot data. This data is based on identifyingthe unique signal pattern of a slap shot.

A slap shot is difficult to perform. It has four stages which areexecuted in one fluid motion to make the puck fly into the net: firstthe player the hockey stick to shoulder height or higher; next theplayer violently “slaps” the ice slightly behind the puck and uses hisweight to bend the stick, storing energy in it similar to the action ofa spring; when the face of the stick blade strikes the puck, the playerrolls his wrists and shifts his weight so that the energy stored in thestick is released through the puck; finally, the player follows through,ending up with the stick pointed towards the desired target. The bendingof the stick gives the slap shot more speed.

Hockey does not currently take into account the effects of slap shots,but such knowledge has the potential to have a lot of value for teams.The teams are able to use the analysis to monitor workloads and reducethe occurrence of injuries as well as monitoring injured players so thatthey can safely return to competition.

In part this invention is predicated on the insight that the defensemenhave higher work-loads than forwards, during training. It may be, thatthe extra load is due to the defensemen taking more slap shots. A slapshot is a very violent event that requires a lot of torque and lateralacceleration.

Aside from this being a big factor in load, defensemen have a muchhigher rate of hip displacement injuries—not a soft tissue injury, butan alteration in the structure of the hip. No one knows the cause ofthis, it may be due to the massive amount of rotational volume beingapplied during slap shots. Having a way to track and quantify thesecould help reduce injury rates and improve play. Using a combination ofaccelerometer and Gyro data a pattern is seen that is easy to identifyin the software.

DETAILED DESCRIPTION OF THE INVENTION

Preferred embodiments of the invention will be described with referenceto the drawings in which:

FIG. 1 illustrates the signals from accelerometers and gyrometers wornby an ice hockey player while skating;

FIG. 2 illustrates the combined signals from gyrometers andaccelerometers illustrating the signal pattern of a slap shot;

FIG. 3 illustrates a flow chart for identifying and counting left andright strides;

FIG. 4 illustrates a graphical display of stride data over a number ofsessions.

The device worn by the players preferably consists of a back mountedunit with inertial sensors, accelerometers measuring 3D acceleration andgyroscopes measuring 3D rotational velocity of the upper torso. Theremay also be a GPS providing velocity from doppler measurements. A unitof this kind is described in the applicants U.S. Pat. No. 8,036,826 thecontent of which is incorporated herein by reference.

The movement parameters are derived from the signal out puts of thevarious sensors and the clock. The data may be presented in any suitabletabular or graphical form and may be synchronised with video of theplayer. The data may be managed in Excel and the processing may takeplace using R and Matlab.

Referring to FIG. 1, the accelerometer captures the lateral forcegenerated by the stride with the gyro showing the sinusoidal swaypattern of the players' shoulders. Each peak/valley in the signalrepresents a single stride (right or left).

Referring to FIG. 2, the RAW trace presents as a distinct pattern thatis able to be identified in the analysis software used by coaches inassociation with the devices as described in U.S. Pat. No. 8,036,826.

FIG. 3 is a flow chart for analysing the data from the data loggers.

The data processing steps are as follows:

-   -   1. 100 Hz (i.e. 100 data points per second) data collected from        athletes is converted from RAW file format to CSV containing 26        parameters (only 15 utilized for this algorithm) using        proprietary software.    -   2. The data is divided into 1 second (100 data point) sections        and the following features generated using both the        accelerometer and gyroscope are calculated for each section:        -   Interquartile range of gyroscope roll axis (1)        -   Variance of sideways accelerometer        -   25^(th) percentile of IMU up        -   Interquartile range of sideways accelerometers        -   Variance of gyroscope roll axis (1)        -   Variance of IMU sideways        -   Variance of IMU forward        -   Minimum of IMU up        -   Variance of IMU up        -   Minimum of sideways accelerometer        -   Interquartile range of IMU up        -   Variance of the resultant of all 3            accelerometers=sqrt(forwards^2+sideways^2+up^2)        -   25^(th) percentile of gyroscope roll axis (1)        -   Mean of IMU up        -   Variance of up accelerometer        -   25^(th) percentile of smooth player load        -   Minimum of up accelerometer        -   Interquartile range of raw player load        -   Maximum of raw player load        -   Variance of raw player load        -   Maximum of smooth player load        -   Mean forward accelerometer    -   3. The features of each data section are inputs to a random        forest model, which was originally built based on a labeled        training set of hockey data. The model enables prediction of        whether each section is or is not linear skating.    -   4. Sections identified as linear skating are further analyzed to        get a stride count. The maximum (left strides) and minimum        (right strides) peaks and valleys in the gyroscope roll axis        that are at least 48 data points apart are counted as individual        strides.

The learning tree algorithm utilized in this process is a 100 treeRandom Forest model in which each tree is set to have a maximum of 500terminal nodes and the minimum size of the terminal nodes is set at 6data points. The Random Forest model developed in this project can berecreated using the CSV file appended to this document. The columns are(from R documentation of getTree code):

-   -   V1: tree number    -   Left daughter: the row where the left daughter node is; 0 if the        node is terminal    -   Right daughter: the row where the right daughter node is; 0 if        the node is terminal    -   Split var: which variable was used to split the node; 0 if the        node is terminal    -   Split point: where the best split is    -   Status: is the node terminal (−1) or not (1)    -   Prediction: the prediction for the node; 0 if the node is not        terminal

In displaying the data as shown in FIG. 4 it is helpful to show thecomparison between left and right leg strides and also show a goodcomparison between parameters across multiple days for both anindividual, team and period report. Preferably the parameters aredisplayed on a simple 1 to 10 scale. This scale is based on a z-scorecalculation, using the athlete's historical average. On the 1 to 10scale, a score of 5 stands for the player hitting their average, witheach increase in value equating to a 0.5 standard deviation (SD)increase. Thus, a score of 7 would mean the athlete was 1 SD above theiraverage. A score of 10 means they were 2.5 standard deviations abovetheir average.

Every parameter is viewable in this format, with the client being ableto select multiple parameters to view at a time. Each parameter on thegraph may be color-coded for easy interpretation:

-   -   1=red    -   2-3=yellow    -   4-6=green    -   7-8=yellow    -   9-10=red

Players or coaches are able to view one or multiple parameters, singleor multiple athletes for single or multiple sessions.

EXAMPLE

PlayerLoad™ triaxial accelerometry is currently used in collegiate andprofessional ice-hockey and other sports (e.g. football and basketball)for physical performance evaluation. Therefore, population-specifictests in field-based settings are necessary in identifying thereproducibility, reliability, and discriminatory capability of PTA forphysical performance assessment associated with athlete practice andcompetition.

The primary aim of this study was to evaluate the reproducibility andreliability of PTA during maneuvers common to game-specific settings incollegiate ice-hockey players. The secondary aim was to assessassociations between identifiable PlayerLoad™ Band zone (PBZ) ranges andeach of these specific ice-hockey maneuvers, in addition to identifyingpotential measurement bias associated with PTA.

Eight Division I male collegiate ice-hockey players volunteered toparticipate in this study (n=8; defensemen=4, forwards=4; ages 21±0.8years, height 184±2 cm, body mass index=25.4±0.4 kg/m²). This samplesize has been shown in the literature to be adequate for measuringreliability. Exclusionary criteria included absence from on-ice skatingover the previous 30-days due to prior or current injury and playersself-reporting their position of goaltender.

Design

Testing was performed during a break in the competitive season and tookapproximately one hour at the University ice-arena. Testing wasperformed in full ice-hockey gear with skates sharpened to gamespecifications. Participants were told to refrain from vigorous exercise24-hours prior to, and eat a light meal two-hours before testing.Individuals were asked to refrain from caffeine, tobacco, and alcohol12-hours before testing.

On the ice, participants were given ten-minutes to go through theirroutine on-ice warm-up. Testing began when participants declared theywere “ready”. Each participant performed nine specific ice-hockey testsin duplicate. Tests included forward and backward acceleration, 60%top-speed, forward and backwards top-speed, repeated-shift, slap-shot,coasting, and bench-sitting. Participants were given a two-minuterecovery period after each trial, and a three-minute recovery periodbetween tests. All protocols were consistent with the literature and arevalid for assessing on-ice performance.

Forward and backward acceleration were assessed having each participantsprint from a stationary start, blue-line to blue-line (distance=17.68m). Each participant started by standing with his front skate directlybehind the start line, stick in hand. Readiness was determined by theparticipant, who accelerated as fast as possible through the finishline.

Forward and backwards top-speed, 60% top-speed, and coasting wereassessed after completion of acceleration testing. Performance wasmeasured by having each participant skate, with a skating start,blue-line to blue-line. Participants were instructed to take one laparound the rink, increasing speed as they approached the start line.Upon reaching the start line, participants were instructed to move asfast as possible, or 60% of top-speed, or coast, depending on the test,and to maintain that speed through the finish line.

Following linear skate testing, participants performed repeated-shift,slap-shot, and bench-sitting tests. Repeated-shift testing was assessedusing the course layout and guidelines established in the literature.Slap-shots were taken by participants in a standing position from theblue-line nearest the goal. Bench-sitting was assessed by havingparticipants sit on the bench for a period of 20-seconds, during whichthey made movements resembling game settings.

Methodology

Video

High definition video was recorded for each player during all testing(Sony HDR-PJ440, Sony Inc., Tokyo, Japan). Video was captured at 60frames/sec to allow accurate syncing with the 100 Hz accelerometer data.Recording occurred at center-ice half way up the bleachers of the arena,with the videographer instructed to frame each participant during alltesting.

Timing System

Time to completion for each trial was recorded by a TC Speed Trap-IIwireless timing system. The photo cells for all the timing gates wereplaced at waist level of participants to ensure the body crossing theline tripped the laser timer. For linear skate testing, timing gateswere placed directly over the center of the blue-line. Timing gates werethen placed on the face-off circle, blue-line, and centerline for therepeated-shift test, as outlined previously. Sticks were kept on the iceto ensure they did not prematurely trip the laser timer.

PlayerLoad™ Triaxial Accelerometry

Accelerometry data was collected at a sample rate of 100 Hz, using aCatapult Optimeye S5 monitor (Catapult Sports). In accordance with themanufacturer guidelines, the monitor was placed between the scapulae ofeach participant in a neoprene undergarment. The aggregated data fromeach axes of the triaxial accelerometer was integrated to create avector magnitude called PlayerLoad™. Expressed in arbitrary units,PlayerLoad™ has been established in the literature to be a highlyeffective means of quantifying external load. Catapult Sprint softwarewas used for data post-processing. Data were cropped using the video andgate time data to establish the start- and end-point of each trial. Thiswas done to ensure that analyses only included PlayerLoad™ dataaccumulated during each trial. During analyses of individual trials inparticipants, we identified PlayerLoad™ Band zone ranges which weremodeled to best fit the aggregated accelerometer trace of eachrespective movement modality for individual tests.

PlayerLoad™ Band Zones

In addition to recording total PlayerLoad™ for each maneuver, we set theOptimeye S5 monitor to trace movements during each task stratified intosix different PlayerLoad™ Band zones. This was done to assess whetherspecific output ranges would relate to specific ice-hockey maneuvers. Weset each PBZ range as follows: Band 1=0.0-0.3; Band 2=0.31-0.6; Band3=0.61-1.8; Band 4=1.81-3.0; Band 5=3.01-5.0; and Band 6=5.01-10.0.

Statistical Analysis

Parametric data are presented as means±SD or sums. The data was normallydistributed. Homogeneity of variance of data was confirmed usingLevene's test. A bout effect for differences in replicated hockey testswas calculated using the mixed-model repeated measures one-way ANOVAtest adjusted for player position (random effect). To assessintersession and intrasession reproducibility and reliability of PTA, weperformed both coefficient of variation (CV) and intraclass correlation(ICC) analyses with 95% confidence limits (CL).

Standard calculation of CV for sums of the sample or individually forbouts 1 or 2 were performed as, (standard deviation/mean)*100.Calculation of CV between bouts was calculated in the following stepsadapted from the methods of Bland and Altman: 14 first, we calculatedwithin subject variance, s=(bout 1-bout 2)2/2; second, we calculated,2/2; lastly, we calculated, =√2/2.

Calculation of ICC was performed using covariance parameter estimatesfrom ANOVA testing carried out as variance/(variance+residual), whichmaintains the necessary statistical relationships consistent with ourchoice of mixed model repeated measures ANOVA model.

To test for the presence of specific forms of systematic error (i.e.bias-proportional and/or fixed) associated with PTA we used reducedmajor axis univariate linear regression to calculate both slopes andintercepts with 95% CL. Coefficient of determination (R²) values wereused to quantify the magnitude of relationships between total playerload recorded for a specific sampling period and each PlayerLoad™ Bandzone stratification. In addition to using CV and ICC for testing therepeatability and reliability of PTA, R² were also used to assess themagnitude of associations between bouts for each applicable hockey task.The alpha was set at 0.05 to determine two-tailed statisticalsignificance. All computations were made using SAS, v.9.4 (SAS InstituteInc., Cary, N.C.).

Results

Each participant completed all ice-hockey tasks in identical order onthe same day for both bouts.

Total Player Load and Time

There was no statistically significant bout effect. Within and betweenbout CV in addition to between bout ICC indicated that recording oftotal load and time for the ice-hockey protocol was highly reproducibleand reliable.

Task Specific Total Player Load

There was no statistically significant bout effect, mean differencesbetween bouts for each ice-hockey maneuver were not significant (Table1). Overall, CV and ICC indicated high reproducibility and reliabilitybetween bouts.

TABLE 1 Total player loads for each ice-hockey movement BoutReproducibility and Reliability Variable All 1 2 Δ P CV Bout1 CV Bout2CV ICC F acceleration 1.56 ± 0.20 1.53 ± 0.20 1.59 ± 0.20 0.06 ± 0.070.37 8.6 12.9 10.8 0.47 (7.4, 9.8) (12.7, 13.1) (10.6, 11.0) (−0.29,0.87)  B acceleration 0.95 ± 0.20 0.99 ± 0.25 0.92 ± 0.25 0.07 ± 0.070.37 13.8  31.7 19.9 0.71  (7.8, 19.8) (30.0, 33.4) (19.3, 20.5) (0.08,0.93) F top speed 0.85 ± 0.16 0.86 ± 0.14 0.84 ± 0.14 0.03 ± 0.04 0.527.5 19.7 14.0 0.72 (6.7, 8.3) (19.1, 20.3) (13.7, 14.3) (0.10, 0.94) Btop speed 0.48 ± 0.16 0.47 ± 0.14 0.49 ± 0.14 0.02 ± 0.02 0.24 2.8 30.630.4 0.94 (2.7, 2.9) (29.0, 32.2) (28.9, 31.9) (0.73, 0.99) 60% topspeed 0.33 ± 0.08 0.34 ± 0.08 0.33 ± 0.08 0.00 ± 0.01 0.76 2.2 31.6 23.60.94 (2.2, 2.2) (29.9, 33.3) (22.7, 24.5) (0.73, 0.99) Slap-shot 0.47 ±0.04 0.48 ± 0.06 0.46 ± 0.06 0.02 ± 0.02 0.28 3.9 14.2 11.5 0.60 (3.8,4.0) (13.9, 14.5) (11.3, 11.7) (−0.11, 0.90)  Repeated-shift 6.06 ± 0.845.97 ± 0.88 6.16 ± 0.88 0.18 ± 0.12 0.18 26.6  15.4 13.1 0.92 (−43.4,96.6)  (15.1, 15.7) (12.9, 13.3) (0.66, 0.98) Ice coasting 0.36 ± 0.320.36 ± 0.23 0.36 ± 0.23 0.00 ± 0.02 0.99 3.7 61.9 52.5 0.97 (3.6, 3.8)(52.4, 71.4) (46.5, 58.5) (0.86, 0.99) Bench sitting 0.41 ± 0.36 0.41 ±0.25 0.41 ± 0.25 0.01 ± 0.02 0.70 4.1 63.2 54.4 0.97 (4.0, 4.2) (53.1,73.3) (47.8, 61.0) (0.86, 0.99) Data are mean ± SD for parametric data.All = combined bout 1 and 2; F = forward; B = backward; Δ = absolutedifference between bout 1 and 2; P = value determined from theF-statistic from the repeated measures ANOVA; CV = coefficient ofvariation, bout 1 to 2; Bout 1 or 2 CV = coefficient of variationbetween subjects; ICC = intraclass correlation coefficient; Inparentheses are 95% confidence limits for respective CV or ICC.Relationship Testing

A major goal of the present study was to assess relationships betweenspecific PlayerLoad™ Band zones and load recorded for each ice-hockeytask. We report R² in Tables 3-5 to assess relationships, and slopes andintercepts with 95% CL to assess potential bias in PlayerLoad™ Band zonemeasurements.

Linear regression between total time and load recorded of allparticipants for all sessions was, R²=0.04 (95% CL 0.0, 0.1).Relationships were similar when performing linear regressions betweenspecific PlayerLoad™ Band zones (e.g. forward acceleration) andaccumulated time for each ice-hockey task, suggesting there wasnegligible statistical dependence between the total time needed toacquire data and recording of data by PlayerLoad™ technology.

Forward Acceleration

Overall, the strongest R² occurred between band zone four and forwardacceleration load for both bouts (Table 2), suggesting band zone fourwas associated with tracking forward acceleration. 95% CL for interceptsand slopes for regressions between zone four and forward accelerationload indicated no fixed nor proportional bias was present in this bandzone measurement. Whereas, 95% CL for intercepts and slopes forregressions of zones three and five suggested there was a presence ofboth fixed and proportional bias during forward accelerationmeasurement, suggesting band zones three and five mayunder/over-estimate forward acceleration.

During repeat testing, intercepts and slopes for relationships betweenbouts presented no evidence of measurement bias for band zones three,four, and five. Although, in comparison to zones three and four whichshowed strong agreement, regression models for zone five weaklyexplained the percentage of variance between bouts.

Backward Acceleration

Band zone three strongly related with backward acceleration load (Table2).

TABLE 2 Relationships between total acceleration load and individualPlayerLoad ™ Band zones Total Acceleration Load Slope Intercept R₂Forward acceleration Bout 1 (n = 8) Zone 3 −0.35 (−0.74, −0.17) 0.59(0.15, 1.02) 0.35 Zone 4 1.16 (0.78, 1.73) −0.32 (−1.05, 0.42) 0.83*Zone 5 0.43 (0.19, 0.99) −0.63 (−1.25, −0.01) 0.13 Bout 2 (n = 8) Zone 3−0.60 (−1.15, −0.31) 1.04 (0.38, 1.71) 0.52* Zone 4 1.54 (0.96, 2.47)−1.03 (−2.23, 0.17) 0.76* Zone 5 0.62 (0.26, 1.49) −0.91 (−1.90, 0.07)0.02 Agreement (1 vs. 2) Zone 3 1.48 (0.81, 2.69) 0.02 (−0.05, 0.10)0.60* Zone 4 1.16 (0.67, 2.01) −0.26 (−1.24, 0.72) 0.67* Zone 5 1.26(0.55, 2.86) 0.04 (−0.07, 0.15) 0.16 Combined (n = 16) Zone 3 −0.49(−0.77, −0.31) 0.83 (0.46, 1.19) 0.31* Zone 4 1.32 (0.98, 1.77) −0.62(−1.23, 0.00) 0.73* Zone 5 0.53 (0.31, 0.90) −0.77 (−1.23, −0.31) 0.07Backward Acceleration Bout 1 (n = 8) Zone 2 0.16 (0.09, 0.28) −0.09(−0.19, 0.01) 0.64* Zone 3 0.94 (0.63, 1.40) −0.03 (−0.39, 0.33) 0.83*Zone 4 0.72 (0.33, 1.59) −0.63 (−1.28, 0.02) 0.25 Bout 2 (n = 8) Zone 20.17 (0.08, 0.36) −0.09 (−0.22, 0.04) 0.35 Zone 3 0.80 (0.39, 1.59) 0.06(−0.56, 0.67) 0.43* Zone 4 0.36 (0.15, 0.89) −0.32 (−0.66, 0.03) 0.02Agreement (1 vs. 2) Zone 2 0.63 (0.29, 1.36) 0.02 (−0.02, 0.06) 0.28Zone 3 0.69 (0.32, 1.51) 0.25 (−0.27, 0.77) 0.26 Zone 4 −0.30 (−0.71,−0.12) 0.05 (−0.03, 0.13) 0.02 Combined (n = 16) Zone 2 0.16 (0.11,0.24) −0.09 (−0.15, −0.03) 0.55* Zone 3 0.82 (0.56, 1.21) 0.05 (−0.27,0.37) 0.52* Zone 4 0.66 (0.40, 1.07) −0.57 (−0.91, −0.24) 0.20 Reducedmajor axis univariate linear regression models between TotalAcceleration Load and individual PlayerLoad ™ Band zones of forward orbackward acceleration. In parenthesis are 95% confidence limits.PlayerLoad ™ Band zone ranges were set as follows in arbitrary units:Band 2 = 0.31 to 0.6; Band 3 = 0.61 to 1.8; Band 4 = 1.81 to 3.0. *p <0.05.

Evidence of fixed or proportional measurement bias was not observed forregressions between zone three and backward acceleration load. Whereas,slope 95% CL for zone two suggested the presence of proportionalmeasurement bias; and, 95% CL for slopes and intercepts for zone fourindicated there may have been either fixed and/or proportionalmeasurement bias present. Zone three was best representative of backwardacceleration.

Intercepts and slopes for relationships between bouts indicated thatzones two and three were not affected by measurement bias; whereas, zonefour may have been associated with proportional measurement bias.However, the regression models for zones two, three, and four weaklyexplained the percentage of variance between bouts.

Forward Top Speed

Regressions between band zone four and forward top-speed load explainednearly the entire variance between factors (Table 3).

TABLE 3 Relationships between total load and top speed, 60% top speed,or slap-shot loading Total Load Slope Intercept R₂ Forward top speedBout 1 (n = 8) Zone 4 0.98 (0.84, 1.15) −0.02 (−0.16, 0.11) 0.98* Bout 2(n = 8) Zone 4 1.34 (0.83, 2.16) −0.34 (−0.91, 0.22) 0.75* Agreement (1vs. 2) Zone 4 0.94 (0.41, 2.12) −0.01 (−0.72, 0.72) 0.17 Combined (n =16) Zone 4 1.11 (0.91, 1.38) −0.15 (−0.36, 0.05) 0.86* Backward topspeed Bout 1 (n = 8) Zone 2 1.47 (0.77, 2.79)  −0.57 (−1.06, −0.08)0.52* Bout 2 (n = 8) Zone 2 1.55 (0.81, 2.97)  −0.65 (−1.19, −0.10)0.51* Agreement (1 vs. 2) Zone 2 1.10 (0.98, 1.24) −0.01 (−0.04, 0.01)0.99* Combined (n = 16) Zone 2 1.51 (1.02, 2.23)  −0.61 (−0.91, −0.31)0.51* 60% Top Speed Bout 1 Zone 3 1.36 (0.67, 2.78) −0.18 (−0.55, 0.20)0.40* Bout 2 Zone 3 1.61 (0.73, 3.53) −0.26 (−0.73, 0.22) 0.24 Agreement(1 vs. 2) Zone 3 0.86 (0.71, 1.06)  0.03 (−0.02, 0.09) 0.96* CombinedZone 3 1.45 (0.92, 2.29) −0.21 (−0.44, 0.03) 0.33* Slap-shot Bout 1 Zone5 1.00 0.00 0.99* Bout 2 Zone 5 1.00 0.00 0.99* Agreement (1 vs. 2) Zone5 0.77 (0.38, 1.58) 0.09 (−0.21, 0.38) 0.39 Combined Zone 5 1.00 0.000.99* Reduced major axis univariate linear regression models betweenTotal Load and individual PlayerLoad ™ Band zones of forward or backwardtop speed, 60% top speed, or slap-shot. In parenthesis are 95%confidence limits. PlayerLoad ™ Band zone ranges were set as follows inarbitrary units: Band 2 = 0.31 to 0.6; Band 3 = 0.61 to 1.8; Band 4 =1.81 to 3.0; Band 5 = 3.01 to 5.0. *p < 0.05.

Although, intercept 95% CL for slopes and intercepts of regressionssuggested that neither proportional nor fixed measurement bias wasassociated with relationships between zone four and total load. Althoughthe agreement between bouts indicated no evidence of proportional orfixed measurement bias, the regression model weakly explained thepercentage variance between repeat measures of zone four.

During repeat testing, intercepts and slopes for relationships betweenbouts presented no evidence of measurement bias for band zones three,four, and five. Although, in comparison to zones three and four whichshowed strong agreement, regression models for zone five weaklyexplained the percentage of variance between bouts.

Backward Top Speed

Linear regressions between band zone two and backward top speed loadsuggested that approximately half of the variance between factors wasexplained by these models (Table 3). Although, intercept 95% CL in thesemodels suggested the presence of fixed measurement bias. During repeattesting, band zone two appeared to be highly reproducible with noevidence of measurement bias between bouts.

60% of Top Speed

Linear regressions between band zone three and 60% top-speed loadindicated that nearly 40% of the variance between factors could beexplained by these models in the absence of measurement bias (Table 3).There also appeared to be strong evidence of reproducibility of zonethree measures, whereby no measurement bias was likely associated withrepeat testing.

Slap-Shot

Robust associations occurred between band zone five and slap-shot load(Table 4). Nearly the entire variance in relationships between zone fiveand slap-shot load were explained by regression models. Despite similarslopes, intercepts, and R² for bout bouts, between bout

agreement appeared low; although, negligible measurement bias wasassociated with repeat testing.

Repeated-Shift

Linear regression between band zone four and repeated-shift loadindicated a high amount of variance between factors was explained inthese models (Table 4).

TABLE 4 Relationships between total load and repeated-shift, icecoasting, or bench sitting Total Load Slope Intercept R2 Repeated-shiftBout 1 (n = 8) Zone 4 0.86 (0.55, 1.33) −3.64 (−5.98, −1.30) 0.80* Bout2 (n = 8) Zone 4 0.75 (0.52, 1.08) −3.05 (−4.80, −1.30) 0.86* Agreement(1 vs. 2) Zone 4 0.76 (0.47, 1.25) 0.43 (−0.22, 1.09) 0.73* Combined (n= 16) Zone 4 0.81 (0.64, 1.03) −3.39 (−4.61, −2.17) 0.82* Ice coastingBout 1 (n = 8) Zone 1 1.00 0.00 0.99* Bout 2 (n = 8) Zone 1 1.00 0.000.99* Agreement (1 vs. 2) Zone 1 0.88 (0.70, 1.10) 0.04 (−0.04, 0.13)0.95* Combined (n = 16) Zone 1 1.00 0.00 0.99* Bench sitting Bout 1 Zone1 1.00 0.00 0.99* Bout 2 (n = 8) Zone 1 1.00 0.00 0.99* Agreement (1 vs.2) Zone 1 0.81 (0.70, 0.95) 0.07 (0.01, 0.13)  0.98* Combined (n = 16)Zone 1 1.00 0.00 0.99* Reduced major axis univariate linear regressionmodels between Total Load and individual PlayerLoad ™ Band zones ofrepeated-shift, ice coasting, or bench sitting. In parenthesis are 95%confidence limits. PlayerLoad ™ Band zone ranges were set as follows inarbitrary units: Band 1 = 0.0 to 0.3; Band 4 = 1.81 to 3.0. *p < 0.05.

Although, intercept 95% CL suggested the presence of fixed measurementbias in these relationships. During repeat testing, measurement bias didnot appear to be present, in addition to R² values which suggested highreproducibility.

Coasting or Bench-Sitting

Strong relationships were observed between total load recorded duringcoasting or bench-sitting and band zones two or three (Table 4),respectively. 95% CL for slopes and intercepts further suggested noevidence of measurement bias was associated with relationships betweenthese factors.

The major findings suggest:

-   -   1) Total PlayerLoad™ or stratified PBZ recorded during the        performance of ice-hockey maneuvers that are common to in-game        competition demonstrate high reproducibility, reliability, and        negligible measurement bias, and    -   2) stratified PBZ can be used to adequately describe external        load associated with movements that are specific to ice-hockey        maneuvers.

From the above it can be seen that this invention provides uniqueinsight into predicting probability of injury and in managing playeravailability.

Those skilled in the art will realise that this invention may beimplemented in embodiments other than those described without departingfrom the core teachings of this invention.

The invention claimed is:
 1. A system for managing ice hockey playeravailability comprising: player worn torso data loggers includingaccelerometers and gyroscopes, each torso data logger being affixed toan upper torso region on a respective player's back; and a computingdevice adapted to receive data from the accelerometer and gyroscope ofeach torso data logger, said computing device being programmed to:analyse the data received from the torso data loggers by identifying aplurality of data points per second in the received data, to identifyand count for each player ice hockey player movements from the datareceived from each respective torso data logger by calculating featuresusing data points in sub-sections of the data, and using the calculatedfeatures to identify and count the ice hockey player movements, and tomeasure a work load associated with such movements for each player fromthe data received from each respective torso data logger, said computingdevice also being programmed to display the data as ice hockey playermovement metrics.
 2. The system as claimed in claim 1 wherein the icehockey player movement metrics include skating load, a count of left andright leg strides, groin load, high intensity skating strides, and slapshots.
 3. The system as claimed in claim 1 wherein the computing deviceis programmed to derive work load of an individual over a period of timefrom the data received from each torso data logger to assess aprobability of injury to the individual.
 4. The system as claimed inclaim 1 wherein the computing device is programmed to derive groin loadfrom the data from each torso data logger and assess groin load as anindicator of a probability of groin injury for each player.
 5. Thesystem as claimed in claim 1 wherein the computing device is programmedto identify and count a number of slap shots executed by each player asa means of assessing the player work load as an indicator of probabilityof injury for each player.
 6. The system as claimed in claim 1, whereineach calculated feature is analyzed by a learning tree algorithm.
 7. Thesystem as claimed in claim 6, wherein the learning tree algorithmcomprises a random forest model.
 8. The system as claimed in claim 6,wherein the learning tree algorithm determines whether or not eachsecond of received data represents linear skating.
 9. The method asclaimed in claim 6, wherein the learning tree algorithm comprises arandom forest model.
 10. The method as claimed in claim 6, wherein thelearning tree algorithm determines whether or not each second ofreceived data represents linear skating.
 11. The method as claimed inclaim 1, wherein each calculated feature is analyzed by a learning treealgorithm.
 12. A method for managing ice hockey player availability, themethod comprising: affixing torso data loggers to a plurality ofplayers, each torso data logger comprising an accelerometer and agyroscope and being affixed to an upper torso region on a respectiveplayer's back; receiving, with a computing device, data from theaccelerometer and gyroscope of each torso data logger; analyzing, withthe computing device, the data received for each layer from eachrespective torso data logger to identify and count ice hockey playermovements and to measure a player work load associated with said playermovements, wherein: the data received is analyzed by identifying aplurality of data points per second in the received data, data points insub-sections of the data are used to calculate features of the receiveddata, and the calculated features are used to identify and count the icehockey player movements; and displaying, using the computing device, thedata as ice hockey player movement metrics.
 13. The method as claimed inclaim 12 wherein the ice hockey player movement metrics include skatingload, a count of left and right leg strides, groin load, high intensityskating strides, and slap shots.
 14. The method as claimed in claim 12further comprising deriving from the data received from each torso datalogger, with the computing device, player work load of an individualplayer over a period of time to assess a probability of injury to theindividual player.
 15. The method as claimed in claim 12 furthercomprising deriving from the data received from each torso data logger,with the computing device, groin load and assess groin load as anindicator of a probability of groin injury for each player.
 16. Themethod as claimed in claim 12 further comprising identifying andcounting, with the computing device using the data received from eachtorso data logger, a number of slap shots executed by each player as ameans of assessing the player work load as an indicator of probabilityof injury for each player.